In the prior art weighted Filtered Back-Projection (WFBP) is currently typically used for reconstructing computed tomography image data (CT image data). The overwhelming majority of manufacturers of computed tomographs (CT) use this algorithm in various versions. These established algorithms are reliable and produce an acceptable image quality with low computing effort.
A disadvantage is that the (weighted) filtered back-projection algorithms cannot be mathematically precisely resolved for multiline systems, resulting in “cone” artifacts, especially in the case of large approach angles, because of approximations used in the algorithms. It also proves to be disadvantageous that all beams are incorporated into the reconstructed image with the same weight; in other words, although individual x-ray beams have a significantly poorer signal-to-noise ratio when scanning an object under examination because of unequal attenuation of the x-rays in the object under examination, this is not taken into account in the reconstruction. In addition, filtered back-projections are inflexible as regards the geometric simulation of the scanning process. Thus the actual spatial expansion of the x-ray focus and of the detector elements plus the gantry rotation of the CT used to obtain the CT projection data result in blurred CT projection data. The known filtered back-projection algorithms do not enable this blurring to be corrected.
Overall, filtered back-projections are now no longer adequate for certain applications as regards the spatial resolution achievable with them, the image noise and thus in the end the image quality.
Statistical reconstruction methods are known as an alternative to the weighted, filtered back-projection methods. These iterative methods are able to reduce “cone” artifacts and/or take account of information from previously reconstructed CT image data. In addition, in these methods the variable statistical quality of the individual measurement beams can be taken account of using variable weighting; in other words, they take account of the actual distribution of the noise in the CT projection data. These statistical, iterative methods enable CT image data to be created with a higher contrast, a higher spatial resolution, a smaller number of artifacts and a better signal-to-noise ratio compared to the filtered back-projection method. However, a crucial disadvantage is the considerably higher computing effort (approximately a factor of 100) for these methods compared to filtered back-projection.